This invention generally relates to digital data processing, and more particularly, to a method and apparatus for rapid evaluation of digital data processing parameters.
A hologram is a three-dimensional record, (e.g., a film record) of a physical system which, when replayed, produces a true three-dimensional image of the system. Holography differs from stereoscopic photography in that a holographic image exhibits full parallax by affording an observer a full range of viewpoints of the image from every angle, both horizontal and vertical, and full perspective (i.e. it affords the viewer a full range of perspectives of the image from every distance from near to far). A holographic representation of an image thus provides significant advantages over a stereoscopic representation of the same image. This is particularly true in medical diagnosis, where the examination and understanding of volumetric data is critical to proper medical treatment.
While the examination of data which fills a three-dimensional space occurs in all branches of art, science, and engineering, perhaps the most familiar examples involve medical imaging where, for example, Computerized Axial Tomography (CT or CAT), Magnetic Resonance (MR), and other scanning modalities are used to obtain a plurality of cross-sectional images of a human body part. Radiologists, physicians, and patients observe these two-dimensional data xe2x80x9cslicesxe2x80x9d to discern what the two-dimensional data implies about the three-dimensional organs and tissue represented by the data. The intergration of a large number of two-dimensional data slices places great strain on the human visual system, even for relatively simple volumetric images. As the organ or tissue under investigation becomes more complex, the ability to properly integrate large amounts of two-dimensional data to produce meaningful and understandable three-dimensional mental images may become overwhelming.
Presently known modalities for generating volumetric data corresponding to a physical system include, inter alia, computerized axial tomography (CAT or CT) scans, magnetic resonance scans (MR), three-dimensional ultra sound (US), positron emission tomography (PET), and the like. Although a preferred embodiment of the present invention is described herein in the context of medical imaging systems which are typically used to investigate internal body parts (e.g., the brain, spinal cord, and various other bones and organs), those skilled in the art will appreciate that the present invention may be used in conjunction with any suitable data set defining any three-dimensional distribution of data, regardless of whether the data set represents a physical system, e.g., numerical, graphical, and the like.
Typical data sets comprise on the order of 10 to 70 (for CT systems) or 12 to 128 (for MR) two-dimensional data slices 14. Those skilled in the art will appreciate that the thickness and spacing between data slices 14 are configurable and may be adjusted by the CT technician. Typical slice thicknesses range from 1.5 to 10 millimeters and most typically approximately 5 millimeters. The thickness of the slices is desirably selected so that only a small degree of overlap exists between each successive data slice.
The data set corresponding to a CT or MR scan is typically reproduced in the form of a plurality (e.g., 50-100) of two-dimensional transparent images which, when mounted on a light box, enable the observer (e.g., physician) to view each data slice. By reviewing a plurality of successive data slicers 14, the observer may construct a three-dimensional mental image or model of the physical system within volume 16. The accuracy of the three-dimensional model constructed in the mind of the observer is a function of the level of skill, intelligence, and experience of the observer and the complexity and degree of abnormality of the body parts within volume 16.
In addition to the use of an angled gantry, other techniques may be employed to produce a data set in which a plane of each data slice is not necessarily parallel to the plane of every other data slice, or not necessarily orthogonal to the axis of the data set; indeed, the axis of the data set may not necessarily comprise a straight line. For example, certain computerized techniques have been developed which artificially manipulate the data to produce various perspectives and viewpoints of the data, for example, by graphically rotating the data. In such circumstances, it is nonetheless possible to replicate the three-dimensional data set in the context of the present invention. In particular, by carefully coordinating the angle at which the object beam is projected onto the film, the plane of a particular data slice may be properly oriented with respect to the plane of the other data slices and with respect to eh axis of the data set, for example by tilting screen 326 about its horizontal or vertical axis. Presently known CT scan systems generate data slices having a resolution defined by, for example, a 256 or a 512 square pixel matrix. Furthermore, each address within the matrix is typically defined by a twelve bit grey level value. CT scanners are conventionally calibrated in Houndsfield Units whereby air has a density of minus 1,000 and water a density of zero. Thus, each pixel within a data slice may have a grey level value between minus 1,000 and 3,095 (inclusive) in the context of a conventional CT system. Because the human eye is capable of simultaneously perceiving a maximum of approximately one hundred (100) grey levels between pure white and pure black, it is desirable to manipulate the data set such that each data point within a slice exhibits one (1) of approximately fifty (50) to one hundred (100) grey level values (as opposed to the 4,096 available grey level values). The process of redefining these grey level values is variously referred to as xe2x80x9cwindowingxe2x80x9d.
The present inventor has determined that optimum contrast may be obtained by windowing each data slice in accordance with its content. For example, in a CT data slice which depicts a cross-section of a bone, the bone being the subject of examination, the relevant data will typically exhibit grey level values in the range of minus 600 to 1,400. Since the regions of the data slice exhibiting grey level values less than minus 600 or greater than 1,400 are not relevant to the examination, it may be desirable to clamp all grey level values above 1,400 to a high value corresponding to pure white, and those data points having grey level values lower than minus 600 to a low value corresponding to pure black.
As a further example, normal grey level values for brain matter are typically in the range of about 40 while grey level-values corresponding to tumorous tissue may be in the 120 range. If these values were expressed within a range of 4,096 grey level values, it would be extremely difficult for the human eye to distinguish between normal brain and tumorous tissue. Therefore, it may be desirable to clamp all data points having grey level values grater than, e.g., 140 to a very high level corresponding to pure white, and to clamp those data points having grey scale values of less than, e.g. minus 30, to a very low value corresponding to pure black. Windowing the data set in this manner contributes to the production of sharp, unambiguous holograms.
In addition to windowing a data set on a slice-to-slice basis, it may also be advantageous, under certain circumstances, to perform differential windowing within a particular slice, i.e. from pixel to pixel. For example, a certain slice or series of slices may depict a deep tumor in a brain, which tumor is to be treated with radiation therapy, for example by irradiating the tumor with one or more radiation beams. In regions which are not to be irradiated, the slice may be windowed in a relatively dark manner. In regions which will have low to mid levels of radiation, a slice may be windowed somewhat more brightly. In regions of a high concentration of radiation, the slice may be windowed even brighter. Finally, in regions actually containing the tumor, the slice may be windowed the brightest.
Diagnostic imaging modalities (i.e. computerized tomography, computerized tomographic angiography, magnetic resonance, magnetic resonance angiography, ultrasound, digital subtraction angiography, etc.) typically acquire complex digital data which is usually, when displayed or printed, processed to map the large dynamic range of the scanner data (typically 12-bit) to that of the display device (typically 8-bits). Processing of the digital data often includes subjecting the data to various control parameters (i.e. windowing, leveling, cropping, etc.) to enhance the clinical utility of the digital data. The data is usually processed in the form of digital images and can contain from one to several hundred individual two-dimensional digital images (called xe2x80x9cslicesxe2x80x9d) in a single volumetric data set.
Typically, a representative digital image through the anatomy is chosen and the control parameters are applied and adjusted to maximize the resulting imagery. The feedback of the results is usually as rapid as possible to aid the real-time adjustment of the control parameters. The chosen control parameters may need to be adjusted for each displayed image or one set of control parameters is often applied through all of the acquired slices. The results are then typically printed to aid diagnosis by the physician or to form a medical record.
Prior art methods exist for displaying representations of slices of processed digital data; however, the operator oftentimes must mentally reconstruct the two-dimensional slices into a volumetric image using his existing knowledge of anatomy. Furthermore, adjusting the parameters of each slice of data, and thereafter combining the slices does not usually adequately reflect the composite parameters for the entire volume of data. Displaying accurate representations of entire volumes (xe2x80x9cvolume renderingxe2x80x9d) of processed digital data is often much more advantageous in that the final picture contains substantial information about every data element within a data volume. If an operator could receive feedback on the effects of selected parameters on the entire resulting volume of digital data, the operator would be able to produce significantly better diagnostic images. Because of the lack of availability of real-time volumetric imaging devices, the two-dimensional projections of the volumetric data that can be computed in real-time are often used to select these parameters. To the extent these parameters are used to produce a static three-dimensional holographic print, the projection of the volumetric data should desirably simulate a view of the hologram.
Projections of the effects of changing parameters on the volumetric data has often been attempted through the use of maximum intensity projections (MIPs), simple averages, threshold averages, opacity functions and/or the like. Each of these methods only provide substantially accurate simulations of the volumetric data under certain conditions and may require significant computer processing power to execute in real-time. For example, averages often provide substantially accurate simulations for very wide windows, while MIPs often provide substantially accurate simulations for very narrow windows of sparse vascular data. Opacity functions are usually used to collapse the image by assigning colors to different tissue classifications according to some previously defined intensity level ranges. In an opacity function, the light flux reflected by the tissue is combined with the light flux transmitted through the tissue. A problem that normally exists is that, for each voxel to have some effect on the final picture, the voxel typically must absorb or scatter some of the rays passing through it without concealing the voxels which lie behind it. However, an opacity function typically calculates the light flux from hypothetical light reflections without the use of actual raw data. A method and apparatus are needed which assigns intensity values to volumetric raw data.
A maximum intensity projection (MIP) is typically a ray casting technique whereby parallel rays are passed from an arbitrarily chosen viewing direction through a full data set. An intensity value is usually calculated for each voxel which is intersected along the ray. The MIP usually proceeds along the ray and compares the intensity values of successive pixels along the array to determine the pixel with the maximum intensity value. Only the maximum intensity value is typically used for the final image at the viewing plane. More particularly, if N is the number of sections of reformations from any one projection, the MIP algorithm typically eliminates all but 1/N, thus retaining approximately 1-2% of the full data set. Thus, the MIP algorithm usually retains, for each MIP projection, only that voxel which appears brightest from a predetermined viewing direction.
However, use of the MIP algorithm typically assumes that the brightest voxels are the most diagnostically important pieces of information. This assumption can oftentimes lead to dangerous results, i.e. a collapsed MIP may superimpose a scalp branch of an external carotid artery onto a brain parenchyma as if it were an intrinsic vessel, when in fact there may be no such connection. Moreover, lower intensity features of MIP images are often lost, leading to, for example, an apparent reduction in vessel diameter, an overestimation of blood turbulence or stenosis and a loss of visualization of smaller slow flowing vessels. Additionally, voxels at an interface may not exclusively belong to one object or another, so their intensity value represents an average of all the material located near that position. Threshold intensity values are typically set to manipulate the volume data at the interface, but the variation in the threshold values may result in artifacts. Therefore, MIPs typically do not provide consistent accurate feedback on the effects of selected parameters on the entire resulting volume of digital data. To produce better holographic images, a method and apparatus for providing accurate feedback on the effects of selected parameters is needed.
The present invention includes a method and apparatus for simulating, for example on a desktop computer, a specific view of a hologram. A preferred embodiment of this invention suitably enables, in medical imaging, the manipulation of intensity transformations (windowing and leveling), regions (cropping) and views (axial, coronal and lateral) and the display of the resulting simulations in substantially real time.
In accordance with one aspect of the present invention, an approximation of substantially accurate pixel intensities is achieved by collapsing three-dimensional data onto a two-dimensional view, without the need for constructing complex summations of fringe patterns, as is typically required when constructing a hologram. A power function is suitably applied to each voxel in the data set. The values for a particular x, y coordinate are then summed along the z axis, with the resultant sum value being stored in a sum buffer for all values of x and y. The maximal value of the sum buffer is determined, then the sum buffer values are normalized by this maximum value. Finally, an inverse power function is applied to the normalized sum, then the results are scaled over the range of values of the output buffer. Consequently, the operator is able to view, in substantially real time, simulations of a single view of a hologram created from the selected parameters.